Breakthrough in Artificial Intelligence: Physarum-Energy Optimization Algorithm Yields High-Quality Results

Research conducted at Burdur Mehmet Akif Ersoy University has resulted in a significant breakthrough in the field of artificial intelligence. The study focused on the traveling salesman problem, a notoriously difficult issue in combinatorial optimization, and proposed a new approach using the physarum-energy optimization algorithm (PEO). This population-based optimization algorithm was applied to the symmetric traveling salesman problem and showed competitive performance compared to other metaheuristics.

Key Takeaways:

• The traveling salesman problem is an NP-hard problem that has been studied extensively by researchers.
• The physarum-energy optimization algorithm (PEO) is a population-based optimization algorithm that uses multiple solutions, conductivities, and parameter strategies to update solutions in each generation.
• The PEO and its hybrids (k-NN, 2-opt, 3-opt, k-opt) were evaluated on several benchmark problems and found to produce high-quality results compared to other metaheuristics such as ant colony optimization, black hole algorithm, tabu search, and whale optimization algorithm.
• The population-based PEOs and their derived forms were found to solve the optimization problem quite competitively in CPU time compared to other test algorithms.

Statistics:

• The PEO and its hybrids were applied to several benchmark problems, resulting in high-quality solutions.
• The computational results show that the PEO and its hybrids can find optimal or near-optimal solutions to the traveling salesman problem.
• The CPU time required to solve the optimization problem was significantly reduced using the PEO and its hybrids.

Sources:

• An artificial intelligence technique: experimental analysis of population-based physarum-energy optimization algorithm. Discover Artificial Intelligence, 2025,5(1):1-19.